A pixel typically represents the smallest area resolvable by a capturing imaging sensor. For some applications, it is desirable for such area to be as small as possible. Other applications, such as land-use classification, operate on a larger fundamental scale. As one example, agricultural land use will generally be uniform over a farmer's property, which will typically occupy many pixels in a satellite image. Unfortunately, operating at the pixel-level in such larger fundamental scale applications can be both/either (1) unnecessarily computationally expensive, and (2) needlessly inaccurate. Processing challenges can be further exacerbated due to factors such as the sheer size of observational datasets. Accordingly, there is an ongoing need for systems and techniques capable of efficiently and accurately processing imagery data.